[Comp-neuro] 2nd CfP: ICML/UAI/COLT 2008 Workshop on Nonparametric Bayes

YeeWhye Teh yeewhye at gmail.com
Wed Apr 30 23:14:44 CEST 2008


This is the second call for abstracts and participation for the workshop on
nonparametric Bayes at ICML/UAI/COLT to be held July 9, 2008.  Apologies for
cross-posting.

Please note the extended submission deadline of May 9, 2008.


Nonparametric Bayes 2008
Workshop held at ICML/UAI/COLT 2008
Helsinki, Finland
July 9, 2008
http://npbayes.wikidot.com

One of the major problems driving current research in statistical machine
learning is the search for ways to exploit highly-structured models that are
both expressive and tractable. Nonparametric Bayesian methodology provides
significant leverage on this problem. In the nonparametric Bayesian
framework, the prior distribution is not a fixed parametric form, but is
rather a general stochastic process—-a distribution over a possibly
uncountably infinite number of random variables. This generality makes it
possible to work with prior and posterior distributions on objects such as
trees of unbounded depth and breadth, graphs, partitions, sets of monotone
functions, sets of smooth functions and sets of general measures.

Applications of nonparametric Bayesian methods have begun to appear in
disciplines such as information retrieval, natural language processing,
machine vision, computational biology, cognitive science and signal
processing. Because of their flexibility, they can also be used to express
prior knowledge without restricting to small parametric classes.
Furthermore, research on nonparametric Bayesian models has served to enhance
the links between statistical machine learning and a number of other
mathematical disciplines, including stochastic processes, algorithms,
optimization, combinatorics and knowledge representation.

There have been several previous workshops on nonparametric Bayesian methods
at machine learning conferences, including workshops at NIPS in 2003 and
2005 and a workshop at ICML workshop in 2006. This workshop aims to build on
the success of these earlier workshops and to catalyze further research.
There are many problem areas that need additional attention; these include
(1) the development of new Monte Carlo and variational algorithms for
inference; (2) the combination of ideas from knowledge representation and
nonparametric Bayesian analysis to develop formal languages for specifying
and manipulating flexible Bayesian models; (3) the problem of finding
objective priors that work in the nonparametric Bayesian setting; (4)
theoretical analysis of the conditions under which nonparametric Bayesian
methods succeed or fail; and (5) the ongoing need to find compelling
applications that serve to exhibit recent developments and to drive further
research. This workshop is intended to bring together the growing community
of nonparametric Bayesian researchers to explore these and other issues.
FORMAT:

The one-day workshop consists of three invited talks, three contributed
talks, a round-table discussion on theory, methodology and applications, a
round-table discussion on general-purpose language and software, a poster
session, and a panel discussion.
CALL FOR PARTICIPATION:

Researchers interested in presenting their work and ideas at the workshop
should send an email to npbayes at googlemail.com with the following
information:

   - Title
   - Authors
   - Abstract (maximum 2 pages, ICML style pdf)
   - Preferred contribution (talk, poster, and/or round-table
   participation)

We expect authors to provide a final version of their papers by late June
for inclusion on the workshop home page. Papers chosen for contributed talks
shall also be expected to liaise with a discussion leader who will be in
charge of stimulating discussion of the work at the workshop.
DATES:

   - Abstracts due: May 9, 2008
   - Notifications: May 16, 2008
   - Final paper due: June 20, 2008
   - Workshop: July 9, 2008

ORGANIZERS:

   - Yee Whye Teh. Gatsby Unit, UCL
   - Romain Thibaux. Computer Science, Berkeley
   - Athanasios Kottas. Applied Mathematics and Statistics, UC Santa Cruz
   - Zoubin Ghahramani. Engineering, Cambridge
   - Michael I. Jordan. Computer Science and Statistics, UC Berkeley

CONTACT:

npbayes at googlemail.com




-- 
Yee Whye Teh, Ph.D. +44 20 7679 1199
Lecturer, Gatsby Computational Neuroscience Unit, University College London
ywteh at gatsby.ucl.ac.uk http://www.gatsby.ucl.ac.uk/~ywteh
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